Devices such as digital cameras and camera phones often provide a face detection mode. In this mode, a camera detects a face in a scene and then automatically focuses (AF) and optimizes exposure (AE). Even if a person moves, the camera will track the detected face, focus on it and adjust exposure accordingly. As result, face detection and tracking provide much convenience to a photographer when taking a portrait scene.
Though the terms of “face detection” and “face tracking” are often undistinguished in product advertisements, they are largely different in a technical view. While face detection typically works in the entire area of the image to discover newly appeared faces, face tracking typically works in a very small neighboring area of an old face and probes its movement. Therefore, a single face tracking may take much less computation (e.g., 2-3 orders of magnitude less) than a full image face detection.
Face detection and tracking features may be implemented in mobile platforms (e.g., cameras and camera phones) as an embedded software solution, or as a hardware-accelerated version (IP core) to provide accelerated speed performance. However, providing these features in an effective manner can be computationally challenging.